Overview

Dataset statistics

Number of variables30
Number of observations661
Missing cells1698
Missing cells (%)8.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory155.0 KiB
Average record size in memory240.2 B

Variable types

Categorical7
Boolean6
Numeric17

Alerts

Code has a high cardinality: 661 distinct values High cardinality
Apgar_1 is highly correlated with Apgar_5High correlation
Apgar_5 is highly correlated with Apgar_1High correlation
ADIR_Soc is highly correlated with QD_LHigh correlation
VABS_Com is highly correlated with VABS_Soc and 6 other fieldsHigh correlation
VABS_Soc is highly correlated with VABS_Com and 6 other fieldsHigh correlation
VABS_Aut is highly correlated with VABS_Com and 6 other fieldsHigh correlation
QD_M is highly correlated with VABS_Com and 6 other fieldsHigh correlation
QD_PS is highly correlated with VABS_Com and 6 other fieldsHigh correlation
QD_L is highly correlated with ADIR_Soc and 7 other fieldsHigh correlation
QD_EH is highly correlated with VABS_Com and 6 other fieldsHigh correlation
QD_R is highly correlated with VABS_Com and 6 other fieldsHigh correlation
Apgar_1 is highly correlated with Apgar_5High correlation
Apgar_5 is highly correlated with Apgar_1High correlation
First_Phrases_Age is highly correlated with VABS_ComHigh correlation
ADIR_Soc is highly correlated with QD_LHigh correlation
VABS_Com is highly correlated with First_Phrases_Age and 7 other fieldsHigh correlation
VABS_Soc is highly correlated with VABS_Com and 6 other fieldsHigh correlation
VABS_Aut is highly correlated with VABS_Com and 6 other fieldsHigh correlation
QD_M is highly correlated with VABS_Com and 6 other fieldsHigh correlation
QD_PS is highly correlated with VABS_Com and 6 other fieldsHigh correlation
QD_L is highly correlated with ADIR_Soc and 7 other fieldsHigh correlation
QD_EH is highly correlated with VABS_Com and 6 other fieldsHigh correlation
QD_R is highly correlated with VABS_Com and 6 other fieldsHigh correlation
Apgar_1 is highly correlated with Apgar_5High correlation
Apgar_5 is highly correlated with Apgar_1High correlation
VABS_Com is highly correlated with VABS_Soc and 4 other fieldsHigh correlation
VABS_Soc is highly correlated with VABS_Com and 3 other fieldsHigh correlation
VABS_Aut is highly correlated with VABS_Com and 1 other fieldsHigh correlation
QD_M is highly correlated with QD_PS and 1 other fieldsHigh correlation
QD_PS is highly correlated with VABS_Com and 5 other fieldsHigh correlation
QD_L is highly correlated with VABS_Com and 4 other fieldsHigh correlation
QD_EH is highly correlated with VABS_Com and 3 other fieldsHigh correlation
QD_R is highly correlated with QD_M and 3 other fieldsHigh correlation
ADIR_quot is highly correlated with ADIR_Soc and 1 other fieldsHigh correlation
Language_Regr is highly correlated with PMD_RegressionHigh correlation
Verbal is highly correlated with Diag_Age and 2 other fieldsHigh correlation
PMD_Regression is highly correlated with Language_RegrHigh correlation
HC is highly correlated with QD_MHigh correlation
Apgar_1 is highly correlated with Apgar_5High correlation
Apgar_5 is highly correlated with Apgar_1High correlation
Diag_Age is highly correlated with VerbalHigh correlation
First_Words_Age is highly correlated with First_Phrases_AgeHigh correlation
First_Phrases_Age is highly correlated with First_Words_Age and 4 other fieldsHigh correlation
ADIR_Soc is highly correlated with ADIR_quot and 3 other fieldsHigh correlation
ADIR_RRB is highly correlated with ADIR_quotHigh correlation
VABS_Com is highly correlated with First_Phrases_Age and 8 other fieldsHigh correlation
VABS_Soc is highly correlated with First_Phrases_Age and 7 other fieldsHigh correlation
VABS_Aut is highly correlated with First_Phrases_Age and 7 other fieldsHigh correlation
QD_M is highly correlated with HC and 7 other fieldsHigh correlation
QD_PS is highly correlated with VABS_Com and 6 other fieldsHigh correlation
QD_L is highly correlated with Verbal and 9 other fieldsHigh correlation
QD_EH is highly correlated with VABS_Com and 6 other fieldsHigh correlation
QD_R is highly correlated with VABS_Com and 6 other fieldsHigh correlation
ADOS_Sev has 104 (15.7%) missing values Missing
ADIR_quot has 18 (2.7%) missing values Missing
Dysmorphysm has 22 (3.3%) missing values Missing
Language_Regr has 36 (5.4%) missing values Missing
Audition has 23 (3.5%) missing values Missing
Vision has 59 (8.9%) missing values Missing
Verbal has 23 (3.5%) missing values Missing
Psyc_Family_Hist has 13 (2.0%) missing values Missing
PMD_Regression has 42 (6.4%) missing values Missing
PMD_Delay has 19 (2.9%) missing values Missing
HC has 81 (12.3%) missing values Missing
Apgar_1 has 179 (27.1%) missing values Missing
Apgar_5 has 42 (6.4%) missing values Missing
Diag_Age has 181 (27.4%) missing values Missing
First_Words_Age has 51 (7.7%) missing values Missing
First_Phrases_Age has 158 (23.9%) missing values Missing
ADIR_Soc has 23 (3.5%) missing values Missing
ADIR_RRB has 24 (3.6%) missing values Missing
ADIR_AbDev has 32 (4.8%) missing values Missing
VABS_Com has 137 (20.7%) missing values Missing
VABS_Soc has 135 (20.4%) missing values Missing
VABS_Aut has 137 (20.7%) missing values Missing
QD_M has 33 (5.0%) missing values Missing
QD_PS has 33 (5.0%) missing values Missing
QD_L has 21 (3.2%) missing values Missing
QD_EH has 33 (5.0%) missing values Missing
QD_R has 33 (5.0%) missing values Missing
Code is uniformly distributed Uniform
Code has unique values Unique
ADIR_AbDev has 20 (3.0%) zeros Zeros
QD_L has 12 (1.8%) zeros Zeros

Reproduction

Analysis started2022-12-16 17:33:01.799066
Analysis finished2022-12-16 17:33:44.012865
Duration42.21 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Code
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct661
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
AA1
 
1
AA650
 
1
AA705
 
1
AA548
 
1
AA561
 
1
Other values (656)
656 

Length

Max length5
Median length5
Mean length4.907715582
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique661 ?
Unique (%)100.0%

Sample

1st rowAA1
2nd rowAA34
3rd rowAA36
4th rowAA72
5th rowAA91

Common Values

ValueCountFrequency (%)
AA11
 
0.2%
AA6501
 
0.2%
AA7051
 
0.2%
AA5481
 
0.2%
AA5611
 
0.2%
AA5661
 
0.2%
AA6131
 
0.2%
AA6351
 
0.2%
AA5831
 
0.2%
AA5931
 
0.2%
Other values (651)651
98.5%

Length

2022-12-16T17:33:44.099860image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
aa11
 
0.2%
aa1661
 
0.2%
aa361
 
0.2%
aa721
 
0.2%
aa911
 
0.2%
aa21
 
0.2%
aa31
 
0.2%
aa61
 
0.2%
aa71
 
0.2%
aa81
 
0.2%
Other values (651)651
98.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Gender
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
M
586 
F
75 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowM
3rd rowM
4th rowF
5th rowM

Common Values

ValueCountFrequency (%)
M586
88.7%
F75
 
11.3%

Length

2022-12-16T17:33:44.225588image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-12-16T17:33:44.749365image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
m586
88.7%
f75
 
11.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ADOS_Sev
Categorical

MISSING

Distinct3
Distinct (%)0.5%
Missing104
Missing (%)15.7%
Memory size5.3 KiB
Autism
443 
ASD
108 
Non Spectrum
 
6

Length

Max length12
Median length6
Mean length5.482944345
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAutism
2nd rowAutism
3rd rowAutism
4th rowAutism
5th rowAutism

Common Values

ValueCountFrequency (%)
Autism443
67.0%
ASD108
 
16.3%
Non Spectrum6
 
0.9%
(Missing)104
 
15.7%

Length

2022-12-16T17:33:44.824275image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-12-16T17:33:44.898051image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
autism443
78.7%
asd108
 
19.2%
non6
 
1.1%
spectrum6
 
1.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ADIR_quot
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.3%
Missing18
Missing (%)2.7%
Memory size5.3 KiB
Positive
579 
Negative
64 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPositive
2nd rowPositive
3rd rowPositive
4th rowPositive
5th rowPositive

Common Values

ValueCountFrequency (%)
Positive579
87.6%
Negative64
 
9.7%
(Missing)18
 
2.7%

Length

2022-12-16T17:33:44.986397image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-12-16T17:33:45.062741image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
positive579
90.0%
negative64
 
10.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Dysmorphysm
Boolean

MISSING

Distinct2
Distinct (%)0.3%
Missing22
Missing (%)3.3%
Memory size1.4 KiB
False
585 
True
 
54
(Missing)
 
22
ValueCountFrequency (%)
False585
88.5%
True54
 
8.2%
(Missing)22
 
3.3%
2022-12-16T17:33:45.107966image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Language_Regr
Boolean

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.3%
Missing36
Missing (%)5.4%
Memory size1.4 KiB
False
550 
True
75 
(Missing)
 
36
ValueCountFrequency (%)
False550
83.2%
True75
 
11.3%
(Missing)36
 
5.4%
2022-12-16T17:33:45.151692image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Audition
Categorical

MISSING

Distinct2
Distinct (%)0.3%
Missing23
Missing (%)3.5%
Memory size5.3 KiB
Normal
624 
Abnormal
 
14

Length

Max length8
Median length6
Mean length6.043887147
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNormal
2nd rowNormal
3rd rowAbnormal
4th rowNormal
5th rowNormal

Common Values

ValueCountFrequency (%)
Normal624
94.4%
Abnormal14
 
2.1%
(Missing)23
 
3.5%

Length

2022-12-16T17:33:45.241551image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-12-16T17:33:45.344939image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
normal624
97.8%
abnormal14
 
2.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Vision
Categorical

MISSING

Distinct2
Distinct (%)0.3%
Missing59
Missing (%)8.9%
Memory size5.3 KiB
Normal
539 
Abnormal
63 

Length

Max length8
Median length6
Mean length6.209302326
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNormal
2nd rowNormal
3rd rowNormal
4th rowNormal
5th rowNormal

Common Values

ValueCountFrequency (%)
Normal539
81.5%
Abnormal63
 
9.5%
(Missing)59
 
8.9%

Length

2022-12-16T17:33:45.439133image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-12-16T17:33:45.532504image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
normal539
89.5%
abnormal63
 
10.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Verbal
Boolean

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.3%
Missing23
Missing (%)3.5%
Memory size1.4 KiB
False
364 
True
274 
(Missing)
 
23
ValueCountFrequency (%)
False364
55.1%
True274
41.5%
(Missing)23
 
3.5%
2022-12-16T17:33:45.578035image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Psyc_Family_Hist
Boolean

MISSING

Distinct2
Distinct (%)0.3%
Missing13
Missing (%)2.0%
Memory size1.4 KiB
False
359 
True
289 
(Missing)
 
13
ValueCountFrequency (%)
False359
54.3%
True289
43.7%
(Missing)13
 
2.0%
2022-12-16T17:33:45.621710image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

PMD_Regression
Boolean

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.3%
Missing42
Missing (%)6.4%
Memory size1.4 KiB
False
589 
True
 
30
(Missing)
 
42
ValueCountFrequency (%)
False589
89.1%
True30
 
4.5%
(Missing)42
 
6.4%
2022-12-16T17:33:45.665227image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

PMD_Delay
Boolean

MISSING

Distinct2
Distinct (%)0.3%
Missing19
Missing (%)2.9%
Memory size1.4 KiB
True
380 
False
262 
(Missing)
 
19
ValueCountFrequency (%)
True380
57.5%
False262
39.6%
(Missing)19
 
2.9%
2022-12-16T17:33:45.709500image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

HC
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct56
Distinct (%)9.7%
Missing81
Missing (%)12.3%
Infinite0
Infinite (%)0.0%
Mean34.69672414
Minimum10
Maximum75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2022-12-16T17:33:45.812043image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile32
Q134
median34.5
Q335.5
95-th percentile37
Maximum75
Range65
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation3.236072529
Coefficient of variation (CV)0.09326737927
Kurtosis89.52121376
Mean34.69672414
Median Absolute Deviation (MAD)1
Skewness6.122048278
Sum20124.1
Variance10.47216542
MonotonicityNot monotonic
2022-12-16T17:33:45.965470image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3492
13.9%
3591
13.8%
34.563
9.5%
3663
9.5%
35.543
6.5%
3342
6.4%
33.531
 
4.7%
3722
 
3.3%
3215
 
2.3%
36.515
 
2.3%
Other values (46)103
15.6%
(Missing)81
12.3%
ValueCountFrequency (%)
101
 
0.2%
252
 
0.3%
261
 
0.2%
27.81
 
0.2%
28.52
 
0.3%
291
 
0.2%
29.52
 
0.3%
303
0.5%
30.52
 
0.3%
317
1.1%
ValueCountFrequency (%)
752
 
0.3%
511
 
0.2%
501
 
0.2%
39.51
 
0.2%
392
 
0.3%
38.52
 
0.3%
386
0.9%
37.81
 
0.2%
37.58
1.2%
37.31
 
0.2%

Apgar_1
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct8
Distinct (%)1.7%
Missing179
Missing (%)27.1%
Infinite0
Infinite (%)0.0%
Mean8.560165975
Minimum3
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2022-12-16T17:33:46.102714image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5
Q19
median9
Q39
95-th percentile10
Maximum10
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.250991446
Coefficient of variation (CV)0.1461410269
Kurtosis4.677027637
Mean8.560165975
Median Absolute Deviation (MAD)0
Skewness-2.175051167
Sum4126
Variance1.564979598
MonotonicityNot monotonic
2022-12-16T17:33:46.204129image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
9335
50.7%
842
 
6.4%
1040
 
6.1%
723
 
3.5%
616
 
2.4%
516
 
2.4%
48
 
1.2%
32
 
0.3%
(Missing)179
27.1%
ValueCountFrequency (%)
32
 
0.3%
48
 
1.2%
516
 
2.4%
616
 
2.4%
723
 
3.5%
842
 
6.4%
9335
50.7%
1040
 
6.1%
ValueCountFrequency (%)
1040
 
6.1%
9335
50.7%
842
 
6.4%
723
 
3.5%
616
 
2.4%
516
 
2.4%
48
 
1.2%
32
 
0.3%

Apgar_5
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)0.6%
Missing42
Missing (%)6.4%
Memory size5.3 KiB
10.0
542 
9.0
 
50
8.0
 
21
7.0
 
6

Length

Max length4
Median length4
Mean length3.875605816
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10.0
2nd row10.0
3rd row10.0
4th row10.0
5th row10.0

Common Values

ValueCountFrequency (%)
10.0542
82.0%
9.050
 
7.6%
8.021
 
3.2%
7.06
 
0.9%
(Missing)42
 
6.4%

Length

2022-12-16T17:33:46.327076image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-12-16T17:33:46.408600image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
10.0542
87.6%
9.050
 
8.1%
8.021
 
3.4%
7.06
 
1.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Diag_Age
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct8
Distinct (%)1.7%
Missing181
Missing (%)27.4%
Infinite0
Infinite (%)0.0%
Mean3.670833333
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2022-12-16T17:33:46.491176image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median3
Q34
95-th percentile7
Maximum8
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.560198897
Coefficient of variation (CV)0.4250258062
Kurtosis0.2416795122
Mean3.670833333
Median Absolute Deviation (MAD)1
Skewness0.8790776438
Sum1762
Variance2.434220598
MonotonicityNot monotonic
2022-12-16T17:33:46.593002image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3152
23.0%
2104
15.7%
499
15.0%
550
 
7.6%
629
 
4.4%
727
 
4.1%
810
 
1.5%
19
 
1.4%
(Missing)181
27.4%
ValueCountFrequency (%)
19
 
1.4%
2104
15.7%
3152
23.0%
499
15.0%
550
 
7.6%
629
 
4.4%
727
 
4.1%
810
 
1.5%
ValueCountFrequency (%)
810
 
1.5%
727
 
4.1%
629
 
4.4%
550
 
7.6%
499
15.0%
3152
23.0%
2104
15.7%
19
 
1.4%

Walk_Age
Real number (ℝ≥0)

Distinct25
Distinct (%)3.8%
Missing6
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean14.61755725
Minimum8
Maximum44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2022-12-16T17:33:46.716606image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile11
Q112
median14
Q316
95-th percentile22
Maximum44
Range36
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.756838432
Coefficient of variation (CV)0.2570086347
Kurtosis9.714428503
Mean14.61755725
Median Absolute Deviation (MAD)2
Skewness2.265653936
Sum9574.5
Variance14.113835
MonotonicityNot monotonic
2022-12-16T17:33:46.842522image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
12124
18.8%
1394
14.2%
1493
14.1%
1578
11.8%
1865
9.8%
1147
 
7.1%
1735
 
5.3%
1634
 
5.1%
1016
 
2.4%
2414
 
2.1%
Other values (15)55
8.3%
ValueCountFrequency (%)
82
 
0.3%
913
 
2.0%
1016
 
2.4%
1147
 
7.1%
12124
18.8%
1394
14.2%
1493
14.1%
14.51
 
0.2%
1578
11.8%
1634
 
5.1%
ValueCountFrequency (%)
441
 
0.2%
361
 
0.2%
331
 
0.2%
321
 
0.2%
304
 
0.6%
262
 
0.3%
252
 
0.3%
2414
2.1%
233
 
0.5%
225
 
0.8%

First_Words_Age
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct40
Distinct (%)6.6%
Missing51
Missing (%)7.7%
Infinite0
Infinite (%)0.0%
Mean24.26557377
Minimum9
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2022-12-16T17:33:46.991469image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile12
Q115
median24
Q330
95-th percentile48
Maximum100
Range91
Interquartile range (IQR)15

Descriptive statistics

Standard deviation12.03054248
Coefficient of variation (CV)0.4957864421
Kurtosis4.334479007
Mean24.26557377
Median Absolute Deviation (MAD)9
Skewness1.533993287
Sum14802
Variance144.7339525
MonotonicityNot monotonic
2022-12-16T17:33:47.131503image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
24128
19.4%
1293
14.1%
1893
14.1%
3677
11.6%
3053
8.0%
1522
 
3.3%
4819
 
2.9%
1417
 
2.6%
1310
 
1.5%
1010
 
1.5%
Other values (30)88
13.3%
(Missing)51
 
7.7%
ValueCountFrequency (%)
910
 
1.5%
1010
 
1.5%
119
 
1.4%
1293
14.1%
1310
 
1.5%
1417
 
2.6%
1522
 
3.3%
165
 
0.8%
172
 
0.3%
1893
14.1%
ValueCountFrequency (%)
1001
 
0.2%
841
 
0.2%
771
 
0.2%
722
 
0.3%
609
1.4%
502
 
0.3%
4819
2.9%
471
 
0.2%
461
 
0.2%
441
 
0.2%

First_Phrases_Age
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct39
Distinct (%)7.8%
Missing158
Missing (%)23.9%
Infinite0
Infinite (%)0.0%
Mean44.75347913
Minimum11
Maximum120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2022-12-16T17:33:47.275752image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile24
Q136
median42
Q348
95-th percentile72
Maximum120
Range109
Interquartile range (IQR)12

Descriptive statistics

Standard deviation16.2469385
Coefficient of variation (CV)0.3630318541
Kurtosis3.226593863
Mean44.75347913
Median Absolute Deviation (MAD)6
Skewness1.302134189
Sum22511
Variance263.9630108
MonotonicityNot monotonic
2022-12-16T17:33:47.420907image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
36121
18.3%
48104
15.7%
6050
 
7.6%
4241
 
6.2%
2439
 
5.9%
3024
 
3.6%
7222
 
3.3%
4016
 
2.4%
1812
 
1.8%
549
 
1.4%
Other values (29)65
9.8%
(Missing)158
23.9%
ValueCountFrequency (%)
111
 
0.2%
121
 
0.2%
161
 
0.2%
1812
 
1.8%
191
 
0.2%
201
 
0.2%
2439
5.9%
261
 
0.2%
281
 
0.2%
291
 
0.2%
ValueCountFrequency (%)
1203
 
0.5%
1081
 
0.2%
967
 
1.1%
847
 
1.1%
801
 
0.2%
781
 
0.2%
7222
3.3%
702
 
0.3%
662
 
0.3%
641
 
0.2%

ADIR_Soc
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct30
Distinct (%)4.7%
Missing23
Missing (%)3.5%
Infinite0
Infinite (%)0.0%
Mean18.97335423
Minimum0
Maximum30
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2022-12-16T17:33:47.558131image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10
Q114
median19
Q325
95-th percentile29
Maximum30
Range30
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.519930442
Coefficient of variation (CV)0.3436361522
Kurtosis-0.903112607
Mean18.97335423
Median Absolute Deviation (MAD)5
Skewness-0.05696923026
Sum12105
Variance42.50949297
MonotonicityNot monotonic
2022-12-16T17:33:47.679526image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
1641
 
6.2%
2837
 
5.6%
1137
 
5.6%
1436
 
5.4%
2235
 
5.3%
1334
 
5.1%
2634
 
5.1%
1532
 
4.8%
1732
 
4.8%
1031
 
4.7%
Other values (20)289
43.7%
ValueCountFrequency (%)
01
 
0.2%
11
 
0.2%
22
 
0.3%
41
 
0.2%
52
 
0.3%
62
 
0.3%
78
 
1.2%
85
 
0.8%
96
 
0.9%
1031
4.7%
ValueCountFrequency (%)
3029
4.4%
2916
2.4%
2837
5.6%
2715
2.3%
2634
5.1%
2530
4.5%
2430
4.5%
2328
4.2%
2235
5.3%
2124
3.6%

ADIR_RRB
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct14
Distinct (%)2.2%
Missing24
Missing (%)3.6%
Infinite0
Infinite (%)0.0%
Mean4.485086342
Minimum0
Maximum23
Zeros6
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2022-12-16T17:33:47.797243image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median4
Q36
95-th percentile8
Maximum23
Range23
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.000435613
Coefficient of variation (CV)0.4460194209
Kurtosis11.71046292
Mean4.485086342
Median Absolute Deviation (MAD)1
Skewness1.899872272
Sum2857
Variance4.001742642
MonotonicityNot monotonic
2022-12-16T17:33:47.905648image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
3178
26.9%
4162
24.5%
591
13.8%
674
11.2%
835
 
5.3%
735
 
5.3%
229
 
4.4%
110
 
1.5%
96
 
0.9%
106
 
0.9%
Other values (4)11
 
1.7%
(Missing)24
 
3.6%
ValueCountFrequency (%)
06
 
0.9%
110
 
1.5%
229
 
4.4%
3178
26.9%
4162
24.5%
591
13.8%
674
11.2%
735
 
5.3%
835
 
5.3%
96
 
0.9%
ValueCountFrequency (%)
231
 
0.2%
122
 
0.3%
112
 
0.3%
106
 
0.9%
96
 
0.9%
835
 
5.3%
735
 
5.3%
674
11.2%
591
13.8%
4162
24.5%

ADIR_AbDev
Real number (ℝ≥0)

MISSING
ZEROS

Distinct8
Distinct (%)1.3%
Missing32
Missing (%)4.8%
Infinite0
Infinite (%)0.0%
Mean4.027027027
Minimum0
Maximum25
Zeros20
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2022-12-16T17:33:48.014843image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q35
95-th percentile5
Maximum25
Range25
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.562162341
Coefficient of variation (CV)0.3879195075
Kurtosis51.4978199
Mean4.027027027
Median Absolute Deviation (MAD)1
Skewness2.994692219
Sum2533
Variance2.440351179
MonotonicityNot monotonic
2022-12-16T17:33:48.117097image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
5295
44.6%
4161
24.4%
387
 
13.2%
233
 
5.0%
126
 
3.9%
020
 
3.0%
66
 
0.9%
251
 
0.2%
(Missing)32
 
4.8%
ValueCountFrequency (%)
020
 
3.0%
126
 
3.9%
233
 
5.0%
387
 
13.2%
4161
24.4%
5295
44.6%
66
 
0.9%
251
 
0.2%
ValueCountFrequency (%)
251
 
0.2%
66
 
0.9%
5295
44.6%
4161
24.4%
387
 
13.2%
233
 
5.0%
126
 
3.9%
020
 
3.0%

VABS_Com
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct92
Distinct (%)17.6%
Missing137
Missing (%)20.7%
Infinite0
Infinite (%)0.0%
Mean63.79770992
Minimum20
Maximum121
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2022-12-16T17:33:48.251432image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile20
Q148
median65
Q380
95-th percentile96.85
Maximum121
Range101
Interquartile range (IQR)32

Descriptive statistics

Standard deviation22.34522107
Coefficient of variation (CV)0.350251147
Kurtosis-0.6024498023
Mean63.79770992
Median Absolute Deviation (MAD)16
Skewness-0.1881658652
Sum33430
Variance499.3089049
MonotonicityNot monotonic
2022-12-16T17:33:48.411263image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2033
 
5.0%
6215
 
2.3%
8014
 
2.1%
7914
 
2.1%
7412
 
1.8%
7511
 
1.7%
9211
 
1.7%
8511
 
1.7%
7711
 
1.7%
4410
 
1.5%
Other values (82)382
57.8%
(Missing)137
 
20.7%
ValueCountFrequency (%)
2033
5.0%
211
 
0.2%
243
 
0.5%
252
 
0.3%
262
 
0.3%
274
 
0.6%
282
 
0.3%
293
 
0.5%
301
 
0.2%
312
 
0.3%
ValueCountFrequency (%)
1211
0.2%
1181
0.2%
1141
0.2%
1131
0.2%
1121
0.2%
1081
0.2%
1072
0.3%
1062
0.3%
1051
0.2%
1041
0.2%

VABS_Soc
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct72
Distinct (%)13.7%
Missing135
Missing (%)20.4%
Infinite0
Infinite (%)0.0%
Mean66.18441065
Minimum20
Maximum106
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2022-12-16T17:33:48.574018image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile31
Q155
median67
Q378
95-th percentile94
Maximum106
Range86
Interquartile range (IQR)23

Descriptive statistics

Standard deviation17.54315019
Coefficient of variation (CV)0.2650646884
Kurtosis0.4238156378
Mean66.18441065
Median Absolute Deviation (MAD)11
Skewness-0.5099935713
Sum34813
Variance307.7621184
MonotonicityNot monotonic
2022-12-16T17:33:48.728379image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2020
 
3.0%
7818
 
2.7%
5817
 
2.6%
6917
 
2.6%
7116
 
2.4%
6816
 
2.4%
6416
 
2.4%
5214
 
2.1%
6614
 
2.1%
7314
 
2.1%
Other values (62)364
55.1%
(Missing)135
 
20.4%
ValueCountFrequency (%)
2020
3.0%
222
 
0.3%
242
 
0.3%
281
 
0.2%
313
 
0.5%
341
 
0.2%
351
 
0.2%
361
 
0.2%
381
 
0.2%
404
 
0.6%
ValueCountFrequency (%)
1061
 
0.2%
1042
 
0.3%
1011
 
0.2%
1001
 
0.2%
991
 
0.2%
975
0.8%
964
0.6%
958
1.2%
945
0.8%
937
1.1%

VABS_Aut
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct76
Distinct (%)14.5%
Missing137
Missing (%)20.7%
Infinite0
Infinite (%)0.0%
Mean60.46564885
Minimum20
Maximum111
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2022-12-16T17:33:48.883944image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile20
Q151
median63
Q375
95-th percentile87
Maximum111
Range91
Interquartile range (IQR)24

Descriptive statistics

Standard deviation19.40010357
Coefficient of variation (CV)0.3208450407
Kurtosis-0.2258103218
Mean60.46564885
Median Absolute Deviation (MAD)12
Skewness-0.5771709256
Sum31684
Variance376.3640185
MonotonicityNot monotonic
2022-12-16T17:33:49.037220image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2044
 
6.7%
6419
 
2.9%
7517
 
2.6%
7215
 
2.3%
6315
 
2.3%
6614
 
2.1%
6014
 
2.1%
5414
 
2.1%
7913
 
2.0%
6213
 
2.0%
Other values (66)346
52.3%
(Missing)137
 
20.7%
ValueCountFrequency (%)
2044
6.7%
221
 
0.2%
231
 
0.2%
243
 
0.5%
252
 
0.3%
261
 
0.2%
271
 
0.2%
282
 
0.3%
293
 
0.5%
301
 
0.2%
ValueCountFrequency (%)
1111
 
0.2%
1021
 
0.2%
1011
 
0.2%
972
0.3%
961
 
0.2%
952
0.3%
941
 
0.2%
931
 
0.2%
914
0.6%
894
0.6%

QD_M
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct100
Distinct (%)15.9%
Missing33
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean87.17117834
Minimum28
Maximum157
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2022-12-16T17:33:49.193072image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum28
5-th percentile50
Q174
median89
Q3101
95-th percentile118.65
Maximum157
Range129
Interquartile range (IQR)27

Descriptive statistics

Standard deviation20.60147594
Coefficient of variation (CV)0.2363335718
Kurtosis0.04931459745
Mean87.17117834
Median Absolute Deviation (MAD)13
Skewness-0.1979370973
Sum54743.5
Variance424.4208107
MonotonicityNot monotonic
2022-12-16T17:33:49.359574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9820
 
3.0%
8318
 
2.7%
10017
 
2.6%
8116
 
2.4%
9014
 
2.1%
9114
 
2.1%
9314
 
2.1%
8814
 
2.1%
8413
 
2.0%
10113
 
2.0%
Other values (90)475
71.9%
(Missing)33
 
5.0%
ValueCountFrequency (%)
281
 
0.2%
291
 
0.2%
353
0.5%
371
 
0.2%
382
 
0.3%
391
 
0.2%
402
 
0.3%
412
 
0.3%
436
0.9%
441
 
0.2%
ValueCountFrequency (%)
1571
0.2%
1511
0.2%
1391
0.2%
1351
0.2%
1332
0.3%
1321
0.2%
1302
0.3%
1292
0.3%
1281
0.2%
1271
0.2%

QD_PS
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct103
Distinct (%)16.4%
Missing33
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean70.34872611
Minimum19
Maximum161
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2022-12-16T17:33:49.522535image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile34
Q155
median71
Q386
95-th percentile104
Maximum161
Range142
Interquartile range (IQR)31

Descriptive statistics

Standard deviation22.22794515
Coefficient of variation (CV)0.3159679837
Kurtosis0.1057248385
Mean70.34872611
Median Absolute Deviation (MAD)16
Skewness0.1665767107
Sum44179
Variance494.0815454
MonotonicityNot monotonic
2022-12-16T17:33:49.675561image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7319
 
2.9%
8115
 
2.3%
5615
 
2.3%
8013
 
2.0%
7813
 
2.0%
4813
 
2.0%
6313
 
2.0%
7113
 
2.0%
9612
 
1.8%
6712
 
1.8%
Other values (93)490
74.1%
(Missing)33
 
5.0%
ValueCountFrequency (%)
191
 
0.2%
212
0.3%
222
0.3%
232
0.3%
23.51
 
0.2%
261
 
0.2%
272
0.3%
283
0.5%
292
0.3%
301
 
0.2%
ValueCountFrequency (%)
1611
0.2%
1551
0.2%
1461
0.2%
1371
0.2%
1231
0.2%
1211
0.2%
1181
0.2%
1161
0.2%
1151
0.2%
1142
0.3%

QD_L
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct126
Distinct (%)19.7%
Missing21
Missing (%)3.2%
Infinite0
Infinite (%)0.0%
Mean63.0078125
Minimum0
Maximum158
Zeros12
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2022-12-16T17:33:49.842555image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile17
Q137
median61
Q389
95-th percentile111.05
Maximum158
Range158
Interquartile range (IQR)52

Descriptive statistics

Standard deviation32.09359993
Coefficient of variation (CV)0.5093590565
Kurtosis-0.6938688201
Mean63.0078125
Median Absolute Deviation (MAD)26
Skewness0.202422355
Sum40325
Variance1029.999156
MonotonicityNot monotonic
2022-12-16T17:33:49.996576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3014
 
2.1%
7612
 
1.8%
012
 
1.8%
5012
 
1.8%
6111
 
1.7%
9211
 
1.7%
4211
 
1.7%
7210
 
1.5%
7910
 
1.5%
4410
 
1.5%
Other values (116)527
79.7%
(Missing)21
 
3.2%
ValueCountFrequency (%)
012
1.8%
81
 
0.2%
101
 
0.2%
112
 
0.3%
122
 
0.3%
12.51
 
0.2%
134
 
0.6%
142
 
0.3%
152
 
0.3%
161
 
0.2%
ValueCountFrequency (%)
1581
0.2%
1521
0.2%
1501
0.2%
1491
0.2%
1441
0.2%
1401
0.2%
1382
0.3%
1361
0.2%
1311
0.2%
1281
0.2%

QD_EH
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct114
Distinct (%)18.2%
Missing33
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean74.62340764
Minimum15
Maximum150
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2022-12-16T17:33:50.167788image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile32.35
Q154
median77
Q394.25
95-th percentile113.65
Maximum150
Range135
Interquartile range (IQR)40.25

Descriptive statistics

Standard deviation25.98411323
Coefficient of variation (CV)0.3482032521
Kurtosis-0.619877287
Mean74.62340764
Median Absolute Deviation (MAD)20
Skewness-0.02261952787
Sum46863.5
Variance675.1741402
MonotonicityNot monotonic
2022-12-16T17:33:50.332396image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8215
 
2.3%
9213
 
2.0%
10012
 
1.8%
8112
 
1.8%
5711
 
1.7%
10111
 
1.7%
7211
 
1.7%
10811
 
1.7%
7611
 
1.7%
4410
 
1.5%
Other values (104)511
77.3%
(Missing)33
 
5.0%
ValueCountFrequency (%)
151
 
0.2%
182
 
0.3%
192
 
0.3%
201
 
0.2%
211
 
0.2%
222
 
0.3%
241
 
0.2%
251
 
0.2%
25.51
 
0.2%
285
0.8%
ValueCountFrequency (%)
1501
 
0.2%
1491
 
0.2%
1441
 
0.2%
1401
 
0.2%
1351
 
0.2%
1331
 
0.2%
1301
 
0.2%
1291
 
0.2%
1283
0.5%
1271
 
0.2%

QD_R
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct121
Distinct (%)19.3%
Missing33
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean81.78264331
Minimum14
Maximum166
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2022-12-16T17:33:50.525738image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile35.35
Q161
median82
Q3100
95-th percentile127
Maximum166
Range152
Interquartile range (IQR)39

Descriptive statistics

Standard deviation27.32016976
Coefficient of variation (CV)0.3340582874
Kurtosis-0.4271409042
Mean81.78264331
Median Absolute Deviation (MAD)19
Skewness0.05279084141
Sum51359.5
Variance746.3916759
MonotonicityNot monotonic
2022-12-16T17:33:50.687742image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7213
 
2.0%
8113
 
2.0%
8213
 
2.0%
8913
 
2.0%
7813
 
2.0%
10012
 
1.8%
9212
 
1.8%
8412
 
1.8%
8010
 
1.5%
9610
 
1.5%
Other values (111)507
76.7%
(Missing)33
 
5.0%
ValueCountFrequency (%)
141
 
0.2%
181
 
0.2%
201
 
0.2%
252
0.3%
262
0.3%
271
 
0.2%
283
0.5%
291
 
0.2%
312
0.3%
324
0.6%
ValueCountFrequency (%)
1661
0.2%
1551
0.2%
1501
0.2%
1482
0.3%
1431
0.2%
1421
0.2%
1401
0.2%
1382
0.3%
1372
0.3%
1362
0.3%

Interactions

2022-12-16T17:33:38.993184image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:03.502356image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:05.622740image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:07.726615image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:10.444849image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:12.627049image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:14.978055image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:17.114773image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:19.517204image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:21.633094image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:23.714122image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:26.036368image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:28.009736image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:30.013788image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:32.119475image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:34.632104image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:36.867257image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:39.126077image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:03.626349image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:05.744846image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:07.849200image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:10.570223image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:12.748974image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:15.100569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:17.243887image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:19.645056image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:21.756346image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:23.825290image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:26.144860image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:28.118463image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:30.136996image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:32.245981image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:34.760348image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:36.993701image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:39.254224image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:03.755430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:05.875671image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:08.536996image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:10.703488image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:13.060852image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:15.230240image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:17.370753image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:19.781767image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:21.879946image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:23.945506image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:26.262269image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:28.236085image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:30.261248image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:32.377859image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:34.890696image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:37.118325image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:39.378036image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:03.879400image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:05.999264image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:08.668920image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:10.834671image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:13.198719image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:15.365722image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:17.500947image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:19.919824image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:22.004944image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:24.065575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:26.379200image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:28.353704image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:30.386321image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:32.508561image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:35.029177image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:37.243481image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:39.511753image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:03.999931image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:06.126179image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:08.799610image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:10.971678image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:13.321539image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:15.484867image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:17.633554image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:20.058798image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:22.135899image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:24.183360image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:26.493945image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:28.468986image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:30.516662image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:32.642781image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:35.178509image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:37.374442image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:39.633748image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:04.117817image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:06.248835image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:08.931457image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:11.089866image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:13.439877image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:15.619610image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:17.753758image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:20.176869image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:22.253080image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:24.310783image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:26.623809image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:28.593867image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:30.635754image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:32.764302image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:35.304127image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:37.494991image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:39.757015image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:04.238204image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:06.370553image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:09.053212image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:11.207429image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:13.567538image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:15.741867image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:17.876804image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:20.295918image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:22.377104image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:24.430011image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:26.742389image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:28.709351image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:30.757924image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:32.888376image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:35.430708image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:37.615629image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:39.884664image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:04.378578image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:06.494561image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:09.179510image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:11.342575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:13.691204image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:15.866856image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:18.003236image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:20.420650image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:22.500529image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:24.551100image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:26.859499image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:28.833275image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:30.882369image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:33.015838image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:35.564796image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:37.740504image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:40.004932image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:04.518346image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:06.611479image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:09.297231image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:11.465810image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:13.805904image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:15.983164image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:18.121095image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:20.536198image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:22.616259image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:24.662925image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:26.967269image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:28.941986image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:31.007997image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:33.136167image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:35.698449image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:37.859307image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:40.138748image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:04.643870image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:06.732135image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:09.429299image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:11.593638image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:13.926642image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:16.107573image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:18.244134image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:20.657103image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:22.735749image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:24.783560image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:27.079742image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:29.054864image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:31.132424image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:33.261518image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:35.830852image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:37.991454image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:40.276087image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:04.757710image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:06.852150image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:09.565048image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:11.715037image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:14.053483image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:16.226656image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:18.367768image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:20.777302image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:22.852301image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:24.917468image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:27.193384image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:29.170525image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:31.249076image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:33.733224image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:35.957743image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:38.111737image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:40.411409image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:04.869347image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:06.966887image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:09.688627image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:11.833252image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:14.177646image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:16.344943image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:18.740784image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:20.894389image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:22.964931image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:25.034866image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:27.303157image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:29.281197image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:31.360083image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:33.852618image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:36.078324image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:38.227523image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:40.533989image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:04.980025image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:07.083527image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:09.804767image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:11.948945image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:14.302680image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:16.464215image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:18.864180image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:21.010033image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:23.077760image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:25.147360image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:27.412490image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:29.390413image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:31.471579image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:33.971011image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:36.199068image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:38.342738image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:40.662956image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:05.108614image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:07.211255image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:09.930419image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:12.084494image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:14.426030image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:16.598887image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:18.991978image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:21.132591image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:23.201007image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:25.542257image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:27.525149image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:29.503917image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:31.597375image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:34.099299image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:36.327882image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:38.469768image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:40.796887image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:05.238442image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:07.340853image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:10.060913image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:12.219688image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:14.561707image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:16.728955image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:19.124041image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:21.259254image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:23.329650image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:25.669785image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:27.646853image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:29.624826image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:31.727327image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:34.232139image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:36.460837image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:38.601176image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:40.929340image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:05.369127image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:07.473180image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:10.194238image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:12.358521image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:14.708459image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:16.861853image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:19.258663image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:21.389669image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:23.465448image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:25.796479image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:27.774544image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:29.748757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:31.857813image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:34.365636image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:36.601536image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:38.732625image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:41.058133image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:05.494215image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:07.598892image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:10.319600image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:12.490036image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:14.844625image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:16.986874image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:19.386211image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:21.508508image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:23.588593image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:25.916309image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:27.891094image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:29.873975image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:31.986114image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:34.498823image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:36.729856image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-16T17:33:38.861434image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-12-16T17:33:50.844881image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-12-16T17:33:51.112125image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-12-16T17:33:51.388785image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-12-16T17:33:51.644473image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-12-16T17:33:51.876902image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-12-16T17:33:41.327852image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-12-16T17:33:42.408815image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-12-16T17:33:42.983881image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-12-16T17:33:43.815086image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

CodeGenderADOS_SevADIR_quotDysmorphysmLanguage_RegrAuditionVisionVerbalPsyc_Family_HistPMD_RegressionPMD_DelayHCApgar_1Apgar_5Diag_AgeWalk_AgeFirst_Words_AgeFirst_Phrases_AgeADIR_SocADIR_RRBADIR_AbDevVABS_ComVABS_SocVABS_AutQD_MQD_PSQD_LQD_EHQD_R
0AA1F<NA>PositiveNoNoNormalNormalNoYesNoYesNaN9.010.02.013.012.084.028.09.02.0NaNNaNNaN47.035.013.028.038.0
1AA34M<NA>PositiveNoNoNormalNormalNoNoNoYesNaN9.010.07.013.0NaNNaN30.08.05.0NaNNaNNaN43.027.018.025.031.0
2AA36M<NA>PositiveNoNoAbnormalNormalNoYesNoYesNaNNaNNaN6.024.0NaNNaN30.08.05.0NaNNaNNaN52.035.08.031.045.0
3AA72F<NA>PositiveYesNoNormalNormalNoYesNoYesNaNNaN10.0NaN32.09.036.026.04.05.0NaNNaNNaN29.021.023.021.020.0
4AA91M<NA>PositiveNoNoNormalNormalNoNoYesNoNaNNaN10.06.015.0NaNNaN28.08.04.0NaNNaNNaN28.019.011.018.026.0
5AA2MAutismPositiveNoNoNormalAbnormalNoNoNoYes34.09.010.03.013.018.0NaN27.04.01.020.020.020.071.043.030.038.042.0
6AA3FAutismPositiveYesNoNormalNormalYesNoNoYesNaNNaN10.04.018.036.042.030.05.03.0NaNNaNNaN62.038.028.047.038.0
7AA6MAutismPositiveNoNoNormalNormalYesYesNoYes33.89.010.03.014.018.036.013.03.04.040.050.054.093.087.074.055.084.0
8AA7MAutismPositiveNoNoNormalNormalNoYesNoYes34.58.08.03.014.036.0NaN24.04.03.020.041.020.097.070.039.051.080.0
9AA8M<NA>PositiveNoNoNormalNormalNoYesNoYes35.4NaN10.05.013.018.048.029.04.05.0NaNNaNNaN43.029.022.029.014.0

Last rows

CodeGenderADOS_SevADIR_quotDysmorphysmLanguage_RegrAuditionVisionVerbalPsyc_Family_HistPMD_RegressionPMD_DelayHCApgar_1Apgar_5Diag_AgeWalk_AgeFirst_Words_AgeFirst_Phrases_AgeADIR_SocADIR_RRBADIR_AbDevVABS_ComVABS_SocVABS_AutQD_MQD_PSQD_LQD_EHQD_R
651AA906MAutismPositiveNoYesNormalNormalNoNoNoNo35.09.010.0NaN14.015.0NaN21.05.05.062.066.058.0NaNNaN0.0NaNNaN
652AA903MAutismNegativeNoYes<NA><NA>NoNoNoNo34.09.010.0NaN14.014.0NaN9.05.02.0NaNNaNNaN88.058.035.063.0100.0
653AA900MNon SpectrumPositiveNoYes<NA><NA>YesYesNoNoNaN9.010.0NaN12.012.024.011.03.05.0NaNNaNNaN89.078.047.092.078.0
654AA922MASDNegativeNaNYes<NA><NA>NoNoYesNo34.510.010.0NaNNaN18.0NaN7.05.04.069.072.069.098.090.095.092.095.0
655AA904MAutism<NA>NoNoNormalNormal<NA>NoNoNaN32.59.010.0NaN11.024.042.0NaNNaNNaN66.068.067.073.052.042.069.069.0
656AA927M<NA>PositiveNoYesNormalNormal<NA>YesYesYes36.09.010.0NaN13.011.024.0NaNNaNNaN79.074.079.076.055.084.081.084.0
657AA938MAutismPositiveNaNNo<NA><NA>NoYesNoNoNaNNaNNaNNaN16.036.060.011.03.0NaN78.068.077.097.073.065.077.097.0
658AA934MAutismPositiveNoYesNormalNormalYesNoNoYes34.59.010.0NaN15.012.0NaN10.03.025.047.063.055.058.040.020.031.034.0
659AA942MASDPositiveNoNo<NA><NA><NA>NoNoNaN34.59.010.0NaN16.036.0NaNNaNNaNNaN63.077.069.0115.085.047.089.085.0
660AA837MAutismPositiveNoNoNormalNormalNoNoNoNo34.510.010.0NaN15.024.048.015.03.05.072.059.058.080.062.064.083.083.0